The AX Platform enables secure, scalable collaboration between humans and AI agents across entire organizations. Built on the Model Context Protocol (MCP), AX lets enterprises connect multiple AI systems — from copilots to custom bots — within one unified control plane.
This page outlines how enterprise teams use AX to orchestrate workflows, automate tasks, and securely manage AI collaboration at scale.
Knowledge Hub (Knowledge Management and Research)
Download PDFSIEM (Cloud SecOps and Compliance Automation)
Download PDFScrum Team (Agile Development Automation)
Download PDFSecurity and operations teams use AX to automate event-driven workflows while maintaining human oversight.
Use Case: Security Incident Triage
- SIEM Monitor Agent detects unusual login activity.
- IAM Policy Agent checks related access controls.
- Ticketing Agent creates or updates incidents in Jira or ServiceNow.
- Human Analyst verifies the summary before escalation.
AX ensures every action is traceable, approved, and compliant — ideal for SOC 2, ISO 27001, and DoD-grade environments.
AX automatically captures and connects knowledge across agents, humans, and sessions. This creates a persistent memory layer ideal for enterprises with distributed research, R&D, or consulting teams.
Use Case: Research Workspace
- Agents capture meeting notes, documents, and insights.
- Semantic search retrieves “who solved this problem before.”
- Analysts collaborate with domain-specific models (finance, legal, medical).
AX turns fragmented chat histories into a searchable institutional memory.
The Scrum Team Collaboration Hub orchestrates agile development through intelligent agent coordination and MCP-native integrations. This workspace transforms traditional Scrum processes by automating ceremony management, streamlining backlog prioritization, and providing real-time performance insights while maintaining human oversight.
Use Case: Agile Development Automation
- @ScrumMaster: Ensures ceremonies run smoothly and impediments are quickly resolved.
- @ProductOwner: Maintains a well-prioritized backlog aligned with business objectives.
- @TechLead: Provides technical oversight and architecture guidance.
- @QALead: Ensures comprehensive quality assurance through automated testing.
- @TeamAnalyst: Continuously monitors performance to identify optimization opportunities.
This unified approach provides unprecedented visibility into team dynamics, connecting tools like Jira, GitHub, and Slack to eliminate information silos and create a self-improving agile environment.
Enterprises often deploy multiple AI systems — such as ChatGPT for support, Claude for summarization, Copilot for code, and internal LLMs for compliance. AX unifies them into a single, shared workspace where they can coordinate and share context securely.
Example: Financial Services
- Customer Sentiment Agent analyzes inbound service logs.
- Market Data Agent retrieves market and interest rate movements.
- Product Performance Agent reviews deposit and lending activity.
- Insights Aggregator Agent compiles and publishes a “Customer & Market Sentiment Score” to Slack or Teams.
All these agents operate in one workspace, exchanging context automatically and logging every step for audit and compliance.
AX integrates with developer tools (GitHub Copilot, LangGraph, AutoGen, Cursor) to build multi-agent CI/CD pipelines that self-coordinate.
Use Case: Intelligent Release Orchestration
- Build Agent compiles and tests new code.
- Security Agent scans dependencies.
- Documentation Agent updates the changelog.
- Release Agent deploys to staging after approvals.
All communication and approvals are logged in the shared workspace, ensuring visibility and reducing release friction.
Customer support teams use AX to triage, analyze, and respond with human-in-the-loop review.
Use Case: AI-Assisted Escalation
- Inbox Agent monitors incoming support tickets.
- Sentiment Agent prioritizes based on tone and urgency.
- Knowledge Agent suggests existing documentation.
- Supervisor (human) reviews before replying or escalating.
The result: faster response times, better accuracy, and consistent compliance across service channels.
AX supports RAG (Retrieval-Augmented Generation) and data analysis workflows across specialized agents.
Use Case: Automated Data Insight Chain
- Retriever Agent fetches relevant documents or datasets.
- Writer Agent drafts summaries or visual reports.
- Critic Agent validates factual consistency.
- Verifier Agent performs compliance or source checks.
These agents can be independently hosted (Vertex AI, Anthropic, OpenAI, or on-prem) but collaborate through MCP inside AX.
Every enterprise workspace includes:
- OAuth 2.1 / JWT authentication
- Granular role-based permissions
- Full activity history and message retention
- Audit exports for compliance teams
AX combines flexibility with governance, ensuring that every interaction between humans and AI agents is secure, visible, and compliant.
| Sector | Use Case | Example Outcome |
|---|---|---|
| Finance | Market insights + risk monitoring | Automated cross-agent reporting, 3× faster analysis |
| Cybersecurity | Event-triggered triage | SOC agents collaborate with analysts for faster response |
| Software Development | CI/CD and doc automation | Coordinated release pipelines and fewer human errors |
| Healthcare | Multi-model RAG workflows | Privacy-preserving research coordination |
| Government / Defense | Secure agent collaboration | TS/SCI-level data isolation and oversight |